Gestational age and Models for predicting Gestational Diabetes Mellitus

doi:10.21203/rs.3.rs-5050182/v1

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Gestational age and Models for predicting Gestational Diabetes Mellitus

https://doi.org/10.21203/rs.3.rs-5050182/v1

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Introduction

Gestational diabetes mellitus (GDM) is generally identified by measuring abnormal maternal glycemic responses to an oral glucose load in late pregnancy (> 0.6 term). However, our preliminary study suggests that GDM could be identified with a high predictive accuracy (96%) in the first trimester (< 0.35 term) by characteristic changes in the metabolite profile of maternal urine. (Koos and Gornbein, 2021) Due to the gestational rise in insulin resistance and the accompanying perturbations of the maternal metabolome, the urinary metabolite algorithm distinguishing GDM versus CON in early gestation likely differs from that in latter gestation.

Objectives

This study was carried out 1) to identify the metabolites of late-pregnancy urine that are independently associated with GDM, 2) to select a metabolite subgroup for a predictive model for the disorder, 3) to compare the predictive accuracy of this late pregnancy algorithm with the model previously established for early pregnancy, and 4) to determine whether the late urinary markers of GDM likely contribute to the late pregnancy decline in insulin sensitivity.

Methods

This observational nested case-control study comprised a cohort of 46 GDM patients matched with 46 control subjects (CON). Random urine samples were collected at ≥ 24 weeks’ gestation and were analyzed by a global metabolomics platform. A consensus of three multivariate criteria was used to distinguish GDM from CON subjects, and a classification tree of selected metabolites was utilized to compute a model that separated GDM vs CON.

Results

The GDM and CON groups were similar with respect to maternal age, pre-pregnancy BMI and gestational age at urine collection [GDM 30.8\(\:\pm\:\)3.6(SD); CON [30.5\(\:\pm\:3.0\:weeks]\). Three multivariate criteria identified eight metabolites simultaneously separating GDM from CON subjects, comprising five markers of mitochondrial dysfunction and three of inflammation/oxidative stress. A five-level classification tree incorporating four of the eight metabolites predicted GDM with an unweighted accuracy of 89%. The model derived from early pregnancy urine also had a high predictive accuracy (85.9%).

Conclusion

The late pregnancy urine metabolites independently linked to GDM were markers for diminished insulin sensitivity and glucose-stimulated insulin release. The high predictive accuracy of the models in both early and late pregnancy in this cohort supports the notion that a urinary metabolite phenotype may separate GDM vs CON across both early and late gestation. A large validation study should be conducted to affirm the accuracy of this noninvasive and time-efficient technology in identifying GDM.

BJ, Gornbein JA. Early pregnancy metabolites predict gestational diabetes mellitus: Implications for fetal programming. Am J Obstet Gynecol 2021;224(2):215.e1-215.e7.

Metabolomics

urine

gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is a common pregnancy complication that signals major risks for future cardiometabolic morbidity in both mother and child. (Koos and Gornbein, 2021; Sweeting et al.,2022; Ye et al., 2022; Xie et al., 2022; Wicklow et al., 2023; Kautzky-Willer et al., 2023; Rudge et al., 2023)

For over 70 years, oral glucose challenges have been used to identify GDM, which is now generally recognized in late gestation by a 2-h, 75 g or 3-h, 100 g oral glucose tolerance test (GTT). While there is little consensus on the diagnostic utility of the 75 g versus 100 g GTT, (ACOG Practice Bulletin no. 190., 2018; US Preventive Services Task Force., 2021; Tsakiridis et al., 2021; Tehrani et al., 2022; Huhn et al., 2018; Mallik et al., 2023) both approaches are onerous due to inconvenient testing, time involved, fasting requirement, and the emotional and physical trauma of repeated venipuncture. Variable processing of blood samples also impairs the accuracy and interpretation of glucose measurements. (Potter et al., 2020; Jamieson et al., 2023) These deficiencies highlight the exigency for a more amenable and reproducible diagnostic tool.

GDM is a heterogenous, multifactorial disorder involving genetic and environmental modulators of insulin production and sensitivity. (Napso et al., 2018; Benhalima et al., 2019; Kotzaeridi et al., 2021; Selen et al., 2022; Sharma et al., 2022; Wang et al., 2023) Metabolomics studies have revealed metabolite perturbations in maternal urine and blood that are associated with GDM and are thus potential identifiers of the disorder. (Mao et al., 2017; Chen et al., 2018; Hou et al., 2018; Alesi et al., 2021; Lopez-Hernandez et al., 2019; Walejko et al., 2020; Zhao et al., 2019; Tian et al., 2021; Lu et al., 2022) However, predictive algorithms involving these markers have lacked sufficient diagnostic accuracy in separating GDM versus non-GDM subjects.

Progress in high-throughput metabolomics, statistical analysis, and machine learning in preliminary studies have enabled the computation of a metabolite model from early pregnancy urine that had a high predictive accuracy (unweighted mean of sensitivity and specificity) of 96.7%. (Koos and Gornbein, 2021) The maternal metabolome changes dynamically and profoundly over the course of gestation with significant changes occurring in over 70% of 48 interrogated metabolic pathways. (Liang et al., 2020) These adjustments in the molecular milieu also perturb the complex interaction of metabolic pathways. In GDM, the alterations in maternal metabolism that emerge during the second trimester can blunt insulin signaling and sensitivity (Fig. 1), which leads to increased insulin resistance and glucose intolerance. (Yang et al., 2018) Thus, the metabolites in late pregnancy that predict GDM may differ from those identified in the first trimester.

This study involves the metabolomics analysis of late-pregnancy urine from the cohort of gravidas who comprised our previous study in early pregnancy. (Koos and Gornbein, 2021) The objectives are to 1) characterize the metabolite phenotype of late-pregnancy urine that is independently associated with GDM, 2) select a subgroup of these metabolites for a predictive model of GDM, 3) compare the accuracy of this late-pregnancy algorithm with that derived from metabolites of the early-pregnancy model.

2.1 Study design.

This observational nested case-control study involved the non-targeted metabolomics identification of small molecules in maternal urine collected at ≥24 weeks’ gestation. The Global Alliance to Prevent Prematurity and Stillbirth (GAPPS; Seattle, WA) supplied the de-identified, randomly collected, and frozen (-80^oC) urine samples and demographic information of patients recruited from antenatal clinics in Washington State. All participating subjects signed written informed consent forms approved by institutional review boards at the University of Washington Medical Center; Swedish Medical Center, Seattle; Yakima Memorial Hospital; and Children’s Hospital Seattle. (Koos and Gornbein, 2021)

2.2 Subjects.

A cohort of healthy singleton gravidas at ≥24 weeks’ gestation provided randomly collected urine samples and underwent GDM screening at ≥24 weeks’ gestation. GDM was diagnosed by institutional criteria using 1-h, 50 g GCT, a 3-h 100g GTT or HbA1c. GDM and CON gravidas were matched by age, pre-pregnancy BMI, and gestational age (GA) at urine collection. A dissimilarity score equation determined the similarity of the match:

Score = (3[∆GA1/SD_GA1+ ∆GA2/SD_GA2)/2] + 2_*Age/SD_age+ 1 ∆BMI/SD_BMI)/6

where ∆ is the absolute difference between a given case and the respective CON for GA1 (urine collected in early gestation), GA2 (urine collected ≥24 weeks’ pregnancy), maternal age, or pre-pregnancy BMI. SD represents the pooled SD of GA1, GA2, age, or BMI with the score in SD (Z) units. The score was calculated for all combinations of the 46 cases with a larger pool of potential controls. The 46 normal gravidas with the smallest scores (best match) were selected. Both GA1 and GA2 were used in matching, but only data from GA2 were involved in the analysis.

2.3 Metabolomics analysis.

Biochemicals (MW<1000) were identified by UPLC-MS/MS with both positive and negative ion mode electron-spray ionization and gas chromatography (Metabolon, Inc., Durham, NC). Log transformed peak areas of metabolites were scaled to the respective median peak area with adjustments for fluid intake.

2.4 Statistical analysis.

Biochemicals endogenous to maternal metabolism were used in the analyses. Xenobiotics and partially characterized molecules were excluded. There were 626 candidate biochemicals.

a) Bivariate analysis. The values for comparing normally distributed continuous variables between groups were computed using using t-tests, while those not normally distributed were calculated by the Wilcoxon rank sum test. The p values for comparing categorical variables (race, ethnicity) were computed by the Fisher exact test. Log scale metabolites for GDM and CON were summarized as means ±SDs with differences nominally significant at P<0.05 since the metabolites had a normal distribution on the log scale

b) Multivariate analysis. Both random forest and gradient boosting models (GBM) were used to choose a subset of the 626 candidate metabolites to distinguish between GDM and controls. The relevant importance of each metabolite was assessed using random forest predictive accuracy, random forest Gini worsening, and gradient boosting relative influence. A consensus ranking using all three of these criteria was used to select a final subset of top-ranked endogenous compounds.

A classification tree using the top-ranked metabolites by the three multivariate criteria was constructed for the final predictive model. Sensitivity, specificity, accuracy (unweighted mean of specificity and sensitivity), and the receiver-operating characteristic area under the curve (ROC_AUC) were computed to evaluate the accuracy of the final model.

Computations were performed using SAS (version 9.4; SAS Institute Inc, Cary, N.C) and R (version 3.5.2; R Project for Statistical Computing, https://www.r-project.org

3.1 Case-control characteristics. The mean dissimilarity score of 0.37 ± 0.20 units confirmed that the GDM-CON pairs were well-matched for mean maternal age (GDM 32 ± 4.7, CON 31.8 ± 4.2 years; P = 0.56), pre-pregnancy BMI (GDM 31.5 ± 6.8, CON 29.9 ± 6.3 kg/m²; P = 0.18), and GA at urine collection (GDM 30.8 ± 3.6, CON 30.5 ± 3.0; P = 0.36). No significant differences were observed for the unmatched parity and race/ethnicity.

3.2 GDM diagnosis. GDM was diagnosed in the 46 cases based on an abnormal GTT in 22 gravidas (47.8%), GCT (>200 mg/dl) in 6 (13%), GCT (171-187 mg/dl) in 15 (32.6%), fasting blood glucose > 95 mg/dl in one (2.2%) and a first trimester HbA1c ≥5.7% in 2 (4.3%). GDM was diagnosed in 31 (67%) subjects ≥24 weeks’ gestation and eight (17.4%) with risk factors <12 weeks’ gestation (Table 1). Maternal glycemia was controlled by diet (47.8%), oral hypoglycemic agents (41.2%), and insulin (10.8%).

3.3 Bivariate analysis. One-metabolite-at-a-time analysis was used to determine the accuracy of the 626 individual endogenous metabolites distinguishing GDM. The top 10 of the 626 metabolites had individual predictive accuracies ranging from 66.3% to 69.6%. The three highest ranked metabolites were 3-hydroxybutyrate, 3-methylhexanolycarnitine, and saccharopine.

3.4 Multivariate analysis. Individual scree plots of the three multivariate criteria ranked metabolites that independently separated GDM from CON. Thirty of the 626 endogenous metabolites comprised the top 30 by at least one of the three criteria, while 13 of the 30 compounds were highly ranked by at least two. All three criteria were used to select the final eight candidates for the late pregnancy model (Table 2). The eight metabolites selected by the three multivariate criteria were among the 11 highest ranked by bivariate analysis.

3.5 Classification Tree. A classification tree analysis of the eight identified compounds selected four metabolites for the final predictive algorithm. The five-level classification tree identified GDM with an accuracy of 89.1% (Table 3, Figure 2).

A second classification tree was created using the seven metabolites of algorithm previously derived from early pregnancy urine (Table 2). This tree comprised only four of the seven metabolites of the first trimester model (Figure 3). Including the remaining three (lanthionine, argininate, 7,8-dihydroneophterin) did not improve the predictive accuracy of 85.9%.

Maternal insulin resistance initially remains unchanged from non-pregnant values or slightly lower before 14 weeks’ gestation; thereafter, insulin resistance progressively elevates to peak levels around 34 weeks’ gestation, as shown in Figure 1.

This decline in insulin sensitivity arises from increased maternal adiposity, placental growth hormone, and other factors. (Catalano et al., 1991; Catalano et al., 2014; Kampmann et al., 2019; Usman et al., 2023) The resulting changes in available substrates and finer tuned controllers determine the composition of the fuel (glucose versus fatty acids) driving mitochondrial energy production. (Randle et al., 1963; Hue at al., 2009; Jeon et al., 2016; Wasserman., 2022; Pappas et al., 2023; Gonzalez-Rellan et al., 2023)

Glucose and fatty acids differ in the metabolic pathways involved in energy production (Figure 4).

For example, glycolysis generates pyruvate, which is converted to acetyl-coenzyme A (CoA). Acetyl-CoA supports the tricarboxylic acid (TCA) cycle, which, in turn, provides the energy that drives mitochondrial ATP production. In contrast, fatty acids are transported to the mitochondria where sequential β-oxidation of 2C units of acyl-CoAs yields one acetyl-CoA and one ATP per cycle. (Lopaschuk et al., 2016; Batchuluum et al., 2018; Houten at al., 2016)

The increased insulin resistance displayed during the second and third trimesters results in a greater contribution of fatty acids to maternal mitochondrial ATP production. By reducing maternal utilization of glucose, this metabolic adaptation promotes placental transfer of glucose from mother to the fetus, which provides critical substrate support for fetal metabolism and growth.

The maternal pancreatic β-cells normally increase insulin secretion to counter the rise in insulin resistance after 14 weeks’ gestation. In GDM, this β-cell compensation is insufficient to counter the gestational fall in insulin sensitivity. (Usman et al., 2023) The ensuing inefficiency in maternal glucose utilization increases maternal 1) insulin requirements, 2) energy dependence on fatty acid substrates, and 3) circulating blood glucose levels.

T2D is associated with an impairment of L-carnitine and acyl-carnitine entry into mitochondria. The resulting elevation in circulating levels signal a dysfunction in mitochondrial oxidation of fatty acids. (Batchuluum et al., 2018; Reuter et al., 2012)

Both early-onset T2D and GDM are associated with higher circulating concentrations of medium-chain (C6-C12) acyl-carnitines. These lipid metabolites diminish both 1) insulin sensitivity and 2) the glucose-stimulated insulin release by pancreatic β-cells and thus contribute to the dysregulation of maternal glycemia after midgestation. (Batchuluum et al., 2018; Roy et al., 2018)

The current study revealed that in late gestation half of the eight consensus metabolites independently linked to GDM involved disruptions in fatty acid catabolism. These included a greater urinary excretion of one ketone (3-hydroxybutyrate), one small chain (<6C) acylcarnitine ester (3-hydroxy-butyrylcarnitine), one medium-chain (6-12C) acylcarnitine (3-methylhexanolylcarnitine), and one medium chain fatty acid metabolite (3-hydroxydodecanedioate). Not surprisingly, fatty acid derivatives were dominant constituents of the predictive model.

4.1 Amino acid metabolism. Lysine catabolism generates saccharopine and 2-aminoadipate. (Scislowski et al., 1994) In nonpregnant subjects elevated fasting levels of plasma 2-aminoadipic acid raised the long-term risk of T2D, (Wang et al., 2013) and in late pregnancy plasma lysine levels correlated positively with GDM. (Park et al., 2015) In the present study urine saccharopine excretion was simultaneously positively associated with GDM, as observed for first-trimester urine. (Koos and Gornbein, 2021) (Table 2) The elevated urinary excretion of saccharopine is consistent with a GDM-associated rise in hepatic and/or renal metabolism of lysine. (Gatrell et al., 2013)

4.2 Inflammation and oxidation. Hyperglycemia and elevated free fatty acids trigger the release of pro-inflammatory mediators and oxidative stressors, which, in turn, promote insulin resistance and impair insulin release by pancreatic β-cells. (Usman et al., 2013; Perry et al., 2015; Plows et al., 2018; Lee et al., 2018; Pinto et al., 2023)

Cohort studies indicate that GDM subjects express proinflammatory perturbations in the first trimester, such as increased plasma cytokines, gut microbial dysbiosis, (Pinto et al., 2023) and characteristic metabolite perturbations in urine. (Koos and Gornbein, 2021) These observations are consistent with the involvement of proinflammatory mediators in early and late gestation. (Kirwan et al., 2002; Piao et al., 2019; Ye et al., 2023)

GABA is expressed in the brain and other tissues, including pancreatic beta cells.(Vincent et al., 1983; Tillakaratne et al., 1995) GABA combines with histidine to form the dipeptide homocarnosine, an antioxidant, anti-inflammatory compound, and an inhibitory neurotransmitter. The inclusion of homocarnosine in the algorithm is consistent with the GDM-associated metabolic disruptions related to increased oxidative stress/inflammation. (Teufel et al., 2003; Peters et al., 2015; Huang et al., 2018)

Plasma acetylcholine derives largely from non-neuronal tissues, including the placenta.( King et al., 1991; Wessler et al., 2001; McLatchie et al., 2021; Wessler et al., 2003) In the current study, urine acetylcholine excretion was positively and independently associated with GDM. Circulating acetylcholine is a marker of inflammation, (Cox et al., 2020) and the raised renal excretion in GDM subjects in late pregnancy is consistent with heightened placental inflammation in this disorder. (Pen et al., 2021)

4.3 Renal glucose excretion. Removing one hydroxyl group from glucose results in 5-anhydroglucitol, a diet-sourced compound that is insignificantly metabolized by human tissues. (Dworacka et al., 2006; Kim et al., 2013; Pramodkumar., 2019) Blood levels of 5-anhydroglucitol are independent of prandial state, body weight or age. Because of renal tubular reabsorption, little of this compound undergoes urinary elimination.

Glucose competes with 5-anhydroglucitol for the renal tubular transporter. In type 2 DM the augmented glucose delivery to the renal tubules reduces renal tubular uptake of 5-anhydroglucitol and thereby 1) increases urinary excretion and 2) reduces circulating levels. Low serum levels of 1-5-anhydroglucitol are markers for intermediate- or short-term hyperglycemia in type 2 DM and are a modest predictor (ROC_AUC ̴0.69%) of GDM. (Ma et al.,2017)

4.4 Comparison Accuracy of prediction model. The computed algorithm derived

from urine metabolites independently linked to GDM in late pregnancy had a high accuracy in separating GDM versus CON (ROC_AU 0.923; accuracy 89.1%). The metabolite model previously derived from early pregnancy urine similarly had a high discriminatory power (ROC_AU 0.88, accuracy 83.9%).

5.1 Clinical Implications. In GDM subjects proinflammatory markers are expressed in the urine, plasma, and gut microbiota during early pregnancy. (Koos and Gornbein,2021; Pinto et al., 2023; Piras et al., 2022) These observations are consistent with GDM subjects having dysbiosis of the microbiome in early pregnancy that contributes to the pathogenesis of disorder. The high accuracy of the computed predictive models in both early and late pregnancy in this cohort suggests that a metabolite phenotype in maternal urine could accurately detect GDM across gestation.

5.2 Research Implications. Alterations consistent with inflammatory processes have been reported for urine, plasma, and feces weeks before the second trimester fall in insulin sensitivity. (Koos and Gornbein, 2021; Pinto et al., 2023; Law et al., 2017; Piras et al., 2022; Bankole et al., 2022; Teixeira et al., 2023; Dias et al., 2023) Thus, metabolite markers of inflammation expressed during early gestation, even before conception, may signal inflammatory contributors to the pathogenesis of GDM. Longitudinal studies should be conducted to: 1) determine the source(s) and cause of inflammation, 2) identify potential preconception markers, and 3) assess the accuracy of metabolite models for predicting GDM.

5.3 Strengths and Limitations

The strengths of this study include the nested, longitudinal design, which facilitated comparisons between early and late pregnancy. Another advantage is the large number of untargeted metabolites with measurements corrected for maternal hydration. Other strengths include the use of consensus multivariate methods and non-logistic regression, which shielded against false positives and provided a smaller array of candidate metabolites from which to derive true positives for the model. The candidate metabolites of the model were functionally related to the putative pathophysiology of insulin resistance and glucose intolerance, which supports the metabolite selections for the model.

This investigation is limited by the relatively small size and variable diagnostic identifiers. Thus, the results should be validated by a much larger study with more uniform criteria for the disorder.

Urine metabolites independently associated with GDM are expressed before the second trimester rise in insulin resistance. In both early and late pregnancy, the included molecules involved in the putative root cause (oxidative stress/inflammation) of insulin resistance and impaired insulin secretion by pancreatic β-cells. The selected metabolites (e.g., medium chain fatty acids, 5-anhydroglucitol) of late pregnancy urine revealed mitochondrial dysfunction, which signaled a further impairment of glucose tolerance. Metabolite models computed from early- or late-pregnancy urine had a high accuracy in identifying GDM subjects. The results provide further optimism for the development of an early, non-invasive test for GDM. These promising observations should be confirmed by a large validation study.

Conflict of Interest Statement

The authors declare that they have no conflicts of interest.

Funding

Funding was obtained from the University of California Technology Development Group.

Author Contribution Statement

AM and BJ conceived and designed the research. OY contributed to the research design and implementation.

JG performed the data analysis.

AM, BU and OY wrote the manuscript.

All authors read and approved the manuscript

Compliance with Ethical Standards

This article does not contain any studies with human and/or animal participants performed by any of the authors. Specimens were obtained from a de-identified biobank as part of the GAPPS (Global Alliance to Prevent Prematurity and Stillbirth) repository, Seattle, WA. Informed consent was obtained from participating subjects under protocals approved by the Institutional review board of the University of Washington Medical Center, Seattle, WA; Swedish Medical Center, Seattle, WA; Yakima Valley Memorial Hospital, Yakima, WA; and Seattle Children’s Hospital, Seattle, WA.

Data Availability Statement

The metabolomics and metadata reported in this paper are available on request from the corresponding author.

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Gestational age and Models for predicting Gestational Diabetes Mellitus

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